摘要
将改进的概率Petri网应用到电力系统故障诊断中,在保证通用性的同时提高了故障诊断结果的准确性.为避免网络末端保护误动作引起的误断,在概率Petri网中引入"非"逻辑关系模型;为降低建模难度和计算复杂度,依据故障报警信息将故障分为3种类型:简单故障、单一复杂故障和多重复杂故障,分别建立相应的诊断模型.综合考虑电力系统继电保护和相应断路器动作的可靠性和灵敏性、多重故障的复杂性以及故障警报信息的不确定性,在基于改进概率Petri网电网故障诊断模型的基础上分析输入弧权值,以增强诊断效果.对于多重复杂故障,为了避免输电网络线众多引起模型过于繁杂的问题,建立元件的各方向诊断模型和综合诊断模型,用吉林省四平地区电力系统故障实例对本文选取的方法进行仿真测试.仿真结果证明,本文方法能够准确有效地识别故障元件,并能在信息不完备的情况下给出正确的诊断结果,具有良好的通用性与容错性.
The improved probabilistic Petri net is applied to the fault diagnosis of power system,which improves the accuracy of the diagnosis results while guaranteeing the generality. To avoid the mistake caused by the misoperation of protection at the end of the network,the non-logical relationship is introduced into the probabilistic Petri net. In order to reduce the difficulty of modeling and computational complexity,the fault is divided into three types according to the fault alarm information: simple fault,single complex fault and multiple complex fault,and the corresponding diagnosis models are established respectively. Considering the reliability and sensitivity of power system relay protection and corresponding circuit breaker actions,the complexity of multiple faults and the uncertainty of fault alarm information,the input arc weights are analyzed based on the improved probabilistic Petri net fault diagnosis model to enhance the diagnosis effect. For multiple complex fault,in order to avoid the problem of complicated models caused by many transmission lines,the diagnostic model and comprehensive diagnostic model of components in all directions are established,and the improvement of the method is simulated and tested with the example of power system faults in Siping area of Jilin Province. The simulation results show that the proposed method can identify fault elements accurately and effectively,and can give correct diagnosis results when the information is incomplete. It has good versatility and fault tolerance.
作者
曲丽萍
刘冲杰
路赵
何昌龙
QU Liping;LIU Chongjie;LU Zhao;HE Changlong(Engineering Training Center of Beihua University,Jilin 132021,China;College of Electrical and Information Engineering,Beihua University,Jillin 132021,China)
出处
《北华大学学报(自然科学版)》
CAS
2020年第1期118-126,共9页
Journal of Beihua University(Natural Science)
基金
国家重点新产品计划项目(2010GRB10003)
吉林省科技发展计划项目(20190102015JH)
北华大学研究生创新项目(2018048)
关键词
概率Petri网
分层诊断
有向弧权值
“非”逻辑模型
参数改进
probabilistic Petri net
classified fault diagnosis
directed arc weight
non-logical model
parameter improvement